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<?php |
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declare(strict_types=1); |
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namespace Np; |
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use Np\exceptions\dtypeException; |
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/** |
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* Convolve |
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* |
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* 1D & 2D SignalProcessing in pure php |
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* |
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* @package Np |
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* @category Scientific Computing |
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* @author ghost (Shubham Chaudhary) |
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* @email [email protected] |
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* @copyright (c) 2020-2021, Shubham Chaudhary |
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*/ |
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class convolve { |
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/** |
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* 1D convolution between a vector v and kernel k, with a given stride. |
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* |
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* @param \Np\vector $v |
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* @param \Np\vector $k |
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* @param int $stride |
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* @return vector |
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* @throws \Exception |
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*/ |
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public static function conv1D(\Np\vector $v, \Np\vector $k, int $stride = 1): vector { |
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$nc = $v->col + $k->col - 1; |
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$r = vector::factory($nc / $stride); |
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for ($i = 0; $i < $nc; $i += $stride) { |
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$jmin = $i >= $k->col - 1 ? $i - ($k->col - 1) : 0; |
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$jmax = $i <= $v->col ? $i : $v->col - 1; |
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$sigma = 0.0; |
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for ($j = $jmin; $j <= $jmax; ++$j) { |
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$sigma += $v->data[$j] * $k->data[$i - $j]; |
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} |
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$r->data[$i] = $sigma; |
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} |
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return $r; |
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} |
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/** |
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* 2D convolution between a matrix ma and kernel kb, with a given stride. |
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* |
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* @param \Np\matrix $ma |
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* @param \Np\matrix $kb |
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* @param int $stride |
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* @return matrix |
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* @throws \Exception |
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*/ |
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public static function conv2D(\Np\matrix $ma, \Np\matrix $kb, int $stride = 1): matrix { |
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$p = $kb->row / 2; |
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$q = $kb->col / 2; |
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$rc = matrix::factory($ma->row / $stride, $ma->col / $stride); |
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for ($i = 0; $i < $ma->row; $i += $stride) { |
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for ($j = 0; $j < $ma->col; $j += $stride) { |
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$sgima = 0.0; |
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for ($k = 0; $k < $kb->row; ++$k) { |
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$x = $i + $p - $k; |
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if ($x < 0 || $x >= $ma->row) { |
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continue; |
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} |
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for ($l = 0; $l < $kb->col; ++$l) { |
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$y = $j + $q - $l; |
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if ($y >= 0 && $y < $ma->col) { |
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$sgima += $ma->data[$x * $ma->col + $y] * $kb->data[$k * $kb->col + $l]; |
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} |
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} |
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} |
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$rc->data[$i * $ma->col + $j] = $sgima; |
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} |
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} |
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return $rc; |
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} |
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} |
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ghostjat /
Np
